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Dotted notation

As explained in Pandas Workout: 200 exercises to make you a stronger data analyst

Dot notation is attribute-access syntax that can retrieve a pandas column when its label is a valid Python identifier and does not conflict with an attribute.

Dotted notation retrieves a pandas DataFrame column with an expression such as df.colname. It is concise and readable, but works only when the label is a valid Python identifier and does not conflict with an attribute. section 2.0

Retrieve a column

For a DataFrame with a column named population, use df.population. Prefer df['population'] when the label contains spaces or other invalid identifier characters, or when attribute-versus-column meaning could be unclear. section 2.0

Keep these distinctions in mind:

  • Use df['x'] to request a column named x; use df.loc['x'] or df.iloc[0] to retrieve a row when x is an index label or positional target. section 2.0
  • Treat dotted notation as a convenience rather than a universal replacement for bracket notation. section 2.0

How Pandas Workout: 200 exercises to make you a stronger data analyst covers it

How deep it goes
Defines column access, contrasts row indexing, and stops short of broader pandas attribute mechanics.
Examples
Uses df.colname, df['x'], df.loc['x'], and df.iloc[0] to contrast column and row retrieval.
What you can do after
Apply it via pandas to retrieve compatible DataFrame columns and choose bracket notation for unsupported labels.
How it connects
Connects pandas DataFrames with bracket notation, row indexing, attributes, and Python identifiers.

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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Pandas Workout: 200 exercises to make you a stronger data analyst. The section links point to the parts of the book it draws on.